Demographic Dividend Economic Development in Arab Countries

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Munich Personal RePEc Archive

Demographic Dividend Economic Development in Arab Countries

Harkat, Tahar and Driouchi, Ahmed

IEAPS, Al Akhawayn University, Ifrane, Morooco

22 November 2017

Online at https://mpra.ub.uni-muenchen.de/82880/

MPRA Paper No. 82880, posted 23 Nov 2017 10:58 UTC

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Demographic Dividend & Economic Development in Arab Countries

By: Tahar Harkat and Ahmed Driouchi, Institute of Economic Analysis and Prospective Studies (IEAPS), Al Akhawayn University, Ifrane, Morocco

Abstract

The demographic dividend is the window of opportunity provided by changes in the age structure of a population. It occurs because of the decline of both fertility and mortality rates. Data from the World Bank are used for descriptive statistics,

regression analyzes with and without robust standard-errors, in addition to performing Granger-Causality tests. The results indicate that estimated time trends for fertility and mortality are significantly decreasing for Arab countries. Findings also indicate that the demographic dividend has occurred in the recent decade in most of Arab countries except for Egypt. This paper shows also the causal links between the dependency ratio (change in the population age structure) and the working age population, unemployment, economic development, government and private expenditures on health and education, education, and female participation in education variables.

Keywords: Demographic Dividend, Arab Countries, Granger Causality.

JEL: J11-J13-O11.

Introduction:

Economies today are more globalized and open to migration in addition to technological and institutional innovation. While most economies have been benefiting from low fertility and mortality rates, others are still seeking to benefit from the shifts that allow demographic dividends with their likely impact on economic development.

Recent studies on the demographic dividend analyzed groups of countries with different income levels, and indicate that low and upper middle income countries are still facing the beginning of this window of opportunity, which is not the case of high income economies (Lee and Mason, 2012). Contributions also indicate that the

demographic transition in emerging countries benefited only Russia, India, and China,

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but not Brazil (Berlin Institute, 2012; Stampe, Porsse, and Portugal, 2011; Brito and Carvalho, 2013) while in developed countries, gains and economic growths account for values that ranges from 5 to 45% (Mason, 2005; Lee & Mason, 2006; 2010;

Mason & Lee, 2007; 2011). But for countries in Sub-Saharan Africa, they did not take advantage from the demographic dividend, as they need reforms to enhance the human capital (Drummond, Thakoor, and Yu, 2014; Loewe, 2007).

During these recent decades, Arab countries have been through a demographic transition. This latter is characterized by the shift from higher rates of fertility as well as higher rates of mortality to lower values, and resulted in a switch from population with large base pyramids, or expansive pyramids, to either constructive or stationary base.

The population size of Arab countries has been growing over the past decades. This is mainly because of the combined effects of the less rapidly declining fertility rates and the rapidly decreasing mortality rates. This is likely to continue in the near future according to population forecasts of the World Bank (2016).

These demographic changes are referred to as demographic dividend or demographic window of opportunity, as more resources are allocated for younger generations in education and health besides higher labor supply. This population transition can achieve rapid economic growth when the dependency ratio, which is the ratio of the non-active population divided by the active population, reaches lower values.

Recent research has been debating the influence of the age structure of a given population on a macroeconomic level. For this, Bloom and Canning (2004)

demonstrates through a cross-country analysis that a promising age structures impact the increase of income per capita as well as income growth.

The current research focuses on providing the potential magnitude of the occurrence of the demographic dividend in Arab economies besides analyzing the effect of the population change on educational and macroeconomic variables.

The questions that could be raised at this stage of the research are:

 Are the trends of fertility rates and mortality rates significantly decreasing in Arab economies?

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 Do Arab population dynamics result in the occurrence of demographic dividend?

 Do the demographic transitions in these countries impact economic growth, educational, and social variables?

This paper introduces a literature review of the demographic dividend. This is

followed by the selected theoretical framework that is used for the empirical methods applied to the data mobilized. The results of the fertility and mortality trends, the estimation of the demographic dividend, and the causalities by the population change in Arab countries are introduced. The last part of the paper focuses on an overall discussion and conclusion.

I. Literature Review:

Kirk (1996) discusses the change of population structure in its theory of demographic transition that occurs when countries have decreasing rates of fertility and decreasing rates of mortality. This change in the population composition generates an economic opportunity of growth, as there will be fewer needs for investments to meet the youngest segment and thus the remaining resources will be targeting family welfare and economic development (Ross, 2004).

Galor and Weil (2000) indicate that within each country, the demographic transition has many stages. The first stage is noticeable when the population growth becomes negatively correlated with the economic development. This is followed by a decline in child mortality besides the decrease of fertility rate. At this latter stage, the children are perceived as “consumption” rather than “investment”, and greater emphasis targets the quality of health and education, which increase the productivity on the longer run (Rosenzweig, 1990; Soares, 2005).

The contributions of Bloom, Williamson (1998) and Bloom, Canning, and Sevilla (2003) indicate that any change in the age composition of a population within a country can have an impact on its economic performance (Williamson and Higgins, 2001; Bloom, Canning, and Sevilla, 2003). Findings also indicate that if the growth rate of the active population is higher than the growth rate of the overall population, it impacts the economic development positively due to a higher labor supply (Bloom et al, 2013; Bloom and Canning, 2003).

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The contributions of Lee (2003) and Galor (2005) show that the increase in the active population results in a decrease of the number of dependents –the populations of the age groups between 0-15 and 60 years or more- within economies. This leads to an enhancement in the economic outputs, savings, and investments. Bloom et al. (2009), Soares and Falcao (2008) indicate that this demographic transition also supports female participation in the labor market besides savings.

Some authors indicate that the demographic transition is the key driver of the success of some Asian countries (Bloom et al., 2000; Mason, 2001) while others expect that this is yet to take place in Africa (Bloom and Sachs, 1998; Bloom et al., 2003).

The demographic transition leads to achieving the demographic dividend (Carvalho and Wong, 1999; Pool, 2007). But in order to achieve this window of opportunity, proper policies are of prime importance, as without monitoring and adapting these policies on the population change, social risks and unemployment may occur (Bloom and Canning, 2000; Bloom et al., 2003, 2007; Lorentzen et al., 2008).

Contributions have been done to test for the occurrence of the demographic dividends in many economies. In the case of India, the change in the population composition has occurred, but it is not homogeneous among all of its states (Thakur, 2012;

Drummond, Thakur, Yu, 2014). Findings also indicate the impact between the change in the age structure and economic development is conditioned by the presence of good policies and how the BIMARU states are willing to reform their economy. But

Majumder (2013) assesses the link between the demographic transition and youth unemployment. Results indicate that if the Indian policy makers do not relook at the human capital development, education, and skill formation, the demographic

opportunity will turn into a threat.

Ven and Smits (2011) assess the demographic dividend in 39 developing countries.

Findings indicate that the demographic transition is currently occurring in developing countries with higher rates than developed countries. In addition to that, a high ratio of working age population relative to total population positively affects the economic growth while it is the opposite of a high ratio of youth or elderly dependency ratio.

The contribution of Medina and Chager (2015) uses panels data model to analyze the elements to be prioritized in the African political agendas to take advantage from the

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demographic dividend as well as to reduce poverty. Results indicate that the Sub- Saharan Africa needs to enhance the employability and human capital throughout education, foster women participation in the job market, besides enhancing health conditions. Drummond, Thakoor, and Yu (2014) support these latter findings.

In the case of Arab countries, some contributions (United Nations, 2003; El-Khouri, 2016; Crane et al., 2011; Englelhardt and Schulz, 2017) indicate a descriptive analysis of the patterns of the demographic change in Arab economies. They also indicate the patterns of the death rates, birth rates, population growth, international migration, fertility rates, and life expectancy besides the trends of the share of the young population.

The United Nations (2016) introduces the occurrence of the demographic dividend in Arab regions. This contribution estimates the time span, or the opening and closing year, of this window of opportunity. For Morocco, Libya, Algeria, and Tunisia the opening year of the demographic dividend is 1981, while the closing year is 2019 for Tunisia, 2021 for Algeria, and 2025 for Morocco and Libya.

Still, there is a lack of contributions that are directly linked to the demographic

dividends in Arab economies besides the lack of contributions that analyze the impact of the demographic transitions on economic, social, and educational variables.

II. Theoretical Framework:

The theoretical framework introduces the demographic transition theory, followed by the definition of the demographic dividend. The last part of this section introduces the theoretical model of the relationship between the income per capita and economic growth, which is the basis of the demographic dividend simulation.

The demographic transition theory was first introduced by Kirk (1996) and defines the evolution or modernization of societies from the pre-modern regime to a post- modern. This is explained by the transition from higher rates of fertility and mortality rates to lower ones besides the increase in life expectancy in a given country. Every country experiences this phenomenon at different time periods. It first started in North Western Europe followed by the Eastern and Southern Europe. But for low-income countries, or developing countries, this demographic transition did not take place until the beginning of the twentieth century (Lee, 2003).

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The contributions of Kirk (1996), Lee (2003), and Davis (1963) divide the

demographic transition into three main stages. The most important characteristics of the first stage are the high fertility rate and high mortality rate. This is followed by the decline of mortality, as a result of health enhancement besides the improvements of agriculture and transports. The final phase is characterized by a decrease in fertility rates.

The population pyramid has different forms in each of these stages. At the beginning, it has a long base, as the median population age is very young. At the second stage, it becomes flatter at its top and the number of young dependents increase. But when fertility rate decreases, the population growth is kept at check, and the median age population becomes higher.

The demographic shift or demographic transition due to the decreasing rates of mortality and fertility can lead to the demographic dividend, which is benefiting from the change of the population composition to reach an accelerated economic growth due to the larger share of the active population and decreasing trends of the number of total dependents within the country (Gribble & Bremner, 2012).

In addition to that, the demographic dividend can also be explained by the reallocation of governments’ expenditures and savings.

The population in a given country is divided into many age-group categories. If we assume that there are only three main sub-groups that are S1, S2 and S3 at the time period t1, these sub-populations size are going to be subject to a change in a different period of time to be 𝑆₁^′, 𝑆₂^′, and 𝑆₃^′ at t2.

The shift of each group size is defined by a change that is represented by the given formula:

∆_𝑛= 𝑆_𝑛^′ − 𝑆_𝑛 𝑆_𝑛

This change suggests that in the case of ∆₁ and ∆₂ are negative, the younger

population at t1 has more education, more health expenditure, and more consumption.

This also indicates that the decrease of the population size of these groups will result

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in more government savings for that country, which will enhance the education and the health for the younger generations at t2.

The demographic dividend is a phenomenon that has a limited period of time, because as the large active or adult population will move to the oldest segment, there will be less cohort that were born during the period of the declining fertility, and concern will relate to taking care of the elderly (Ross, 2004).

Some contributions indicate that the demographic dividend needs to be accompanied by good policy choices so that economies can take advantage from it rather than being subject to economic and social threats such as unemployment. Bloom et al. (2002) indicates that in order to translate the demographic into a gift for any economy, there should be a prioritization of some variables such as health, education, and family planning. This depends only on the institutional environment and the established policies.

The estimation of the relationship between the per capita income and economic growth is borrowed from the model of Barro and Sala-i-Martin (1995, 2004). This model is used in several other contributions (Mody & Aiyar, 2011; Bloom and Canning, 2004).

The model uses a conditional convergence equation to derive this relationship by the use of the following formula:

𝑔𝑧 = 𝜆(𝑧^∗− 𝑧0) Where:

𝑧^∗: is the steady state of the income per worker;

𝑧₀: is the initial income per worker;

𝑔𝑧: is the growth of income per capita;

and 𝜆: is the speed in which the country converges to its steady state level.

As the steady state of income per worker is defined by the use of many variables that impact the productivity, the formula is rearranged to be:

(1) 𝑔_𝑧= 𝜆(𝑥𝛽 − 𝑧₀)

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x represents all the variables that affect the workers’ productivity and 𝛽 represents its corresponding coefficients.

Bloom and Canning (2004) theorized the relationship between the working age population or active population and the economic growth using variables of interest.

This latter model is given in the following formula:

(2) ^𝑌

𝑁=^𝑌_𝐿_𝑊𝐴^𝐿 ^𝑊𝐴_𝑁

where the GDP per capita is written in terms of total income (Y) divided by the total population (N). This formula is further expanded in terms of labor force (L) and working age population (WA).

When substituting for:

log⁡(_𝑁^𝑌) = 𝑦; log (^𝑌_𝐿) = 𝑧; log (_𝑊𝐴^𝐿 ) = 𝑝; log⁡(^𝑊𝐴_𝑁) = 𝑤 Formula (2) becomes:

𝑦 = 𝑧 + 𝑝 + 𝑤

Assuming the labor force absorption rate, or the labor force divided by the working age, is constant, the formula in terms of growth is:

(3) 𝑔(𝑦) = 𝑔(𝑧) + 𝑔(𝑤)

When substituting formula 1 and 2 into 3, the resulted formula explains the per capita income in terms of initial and growth rate of the working age share, initial and growth rate of the per capita income besides many human productivity factors. Thus the formula will be:

𝑔(𝑦) = 𝜆(𝑥𝛽 − 𝑧0) + 𝑔(𝑤)

(4) 𝑔(𝑦) = 𝜆(𝑥𝛽 + 𝑝 + 𝑤₀− 𝑧₀) + 𝑔(𝑤)

Equation 4 is the basis of the empirical estimation. The assumptions to be made, relate to savings and health. This means that the working population has positive savings while the dependents, either young or old, spend more than they earn. In addition to that, the working population is considered to be healthier than the other

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remaining segments. For this, these variables will not be captured. Aiyar and Mody (2013) estimate the specification forms using the following formula:

(5) 𝑔(𝑦𝑡) = 𝜌 ln(𝑦𝑡) + 𝛽1ln(𝑤𝑡) + 𝛽2ln(𝑔(𝑤𝑡)) + 𝛾^′𝑥𝑡+ 𝑓𝑡+ 𝜂𝑡+ 𝜀𝑡

𝑔(𝑦_𝑖,𝑡) is the dependent variable, which is the growth rate of per capita income, 𝑓_𝑡 is the time invariant fixed effect, 𝜂_𝑡 is a time dummy that captures the effects unique to the decade beginning in year t.

Considering the counterfactual where there is no change in the working age ratio between the base period t=0 and t+n, 𝑤_𝑡 = 𝑤₀, and 𝑔(𝑤_𝑡) = 0. This can be written such as:

(6) 𝑔(𝑦𝑡) = 𝜌 ln(𝑦𝑡) + 𝛽1ln(𝑤0) + 𝛾^′𝑥𝑡+ 𝑓𝑡+ 𝜂𝑡+ 𝜀𝑡

This model defines the demographic dividend as the difference between equation 5 and equation 6, that is:

𝐷𝐷𝑡 = 𝛽1(ln(𝑤𝑡) − ln(𝑤0)) + 𝛽2(𝑔(𝑤𝑡))

This demographic dividend (𝐷𝐷_𝑡) represents the increment of per capita income that is attributed to the change in the age structure.

III. Empirical Investigation 1. Data and methods:

This paper aims at identifying the demographic dividend in Arab countries with comparison to ECE countries. For this, this contribution is divided into three parts.

The first part relates to the analysis of the trends of both fertility and mortality per 1000 infant rates. This is through two regression models that are given such as:

𝑌𝑖 = 𝛼 + 𝛽1𝐹𝑖+ 𝜀 𝑌_𝑖 = 𝛼 + 𝛽₁𝑀_𝑖 + 𝜀

Where:

Y: is the independent variable, which represents years, 𝛼: the intercept,

𝛽: the coefficient that corresponds to each variable, 𝐹𝑖: fertility rate at year i,

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𝑀𝑖: mortality rate at year i, 𝜀: standard error.

The second part summarizes the estimations of the demographic dividend for Arab and ECE countries. Regressions of the theoretical model explained under the demographic dividend simulation section in this part are estimated with heteroskedasticity-robust standard errors.

The data used for the simulation of the demographic dividend are GDP growth per year, log of the GDP per capita, log of the initial working age ratio, and the yearly growth of the working age ratio.

The third part gives the results of the Granger causality test that enables the prediction of the causality between two variables in a sense where a variable enhance the

accurateness of the forecast of the other variable. This section tests different sets of hypotheses and analyzes the causal links between the change in the population age structure that is represented by the dependency ratio, and social, educational, and macroeconomic variables.

The data used are extracted from the World Bank and are of the period between 1960 and 2016. The selected Arab countries are: Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, United Arab Emirates, Palestine, and Yemen, and the selected ECE countries are: Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia

2. Hypotheses to be tested

a. Granger causality between dependency ratio and employment variables:

 H0: Total labor force does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause total labor force.

 H0: Female labor force does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause female labor force.

 H0: Total unemployment does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause total unemployment.

 H0: Young female unemployment does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause young female unemployment.

 H0: Young male unemployment does not Granger cause dependency ratio.

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HA: Dependency ratio does not Granger cause young male unemployment.

 H0: Youth labor force participation does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause youth labor force participation.

b. Granger causality between dependency ratio and economic development variables:

 H0: GDP per capita does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause GDP per capita.

 H0: GDP per capita growth does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause GDP per capita growth.

 H0: Gross savings does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause gross savings.

 H0: Agriculture value added does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause agriculture value added.

 H0: Industry value added does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause industry value added.

c. Granger causality between dependency ratio and expenditure variables:

 H0: Education expenditure does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause education expenditure.

 H0: Health expenditure per capita does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause health expenditure per capita.

 H0: Private health expenditure per capita does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause private health expenditure per capita.

 H0: Public health expenditure per capita does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause public health expenditure per capita.

 H0: Total health expenditure does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause total health expenditure.

d. Granger causality between dependency ratio and educational variables:

 H0: Enrolment in primary education does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause enrolment in primary education.

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 H0: Enrolment in secondary education does not Granger cause enrolment in secondary education.

HA: Dependency ratio does not Granger cause enrolment in secondary education.

 H0: Enrolment in secondary vocational education does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause enrolment in secondary vocational education.

 H0: Enrolment in secondary general education does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause enrolment in secondary general education.

e. Granger causality between dependency ratio and female participation in education variables:

 H0: Female enrolment in primary education does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause female enrolment in primary education.

 H0: Female enrolment in secondary education does not Granger cause enrolment in secondary education.

HA: Dependency ratio does not Granger cause female enrolment in secondary education.

 H0: Female enrolment in secondary vocational education does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause female enrolment in secondary vocational education.

 H0: Female enrolment in secondary general education does not Granger cause dependency ratio.

HA: Dependency ratio does not Granger cause female enrolment in secondary general education.

IV. Results

Two major sets of results are respectively introduced. The first set focuses on the estimation of time trends in variables. The second set of results introduces the links between demographic, economic and social variables.

I. Results for Time Trends in Variables

The variables analyzed are fertility, mortality and demographic dividends.

1. Fertility rates in Arab countries

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Table 1 shows the results of the trends of fertility rate in Arab countries. Findings indicate that all the resulted model for Arab countries are explained by an R-square 0.713 and 0.982 and are significant. The trends of the fertility rate are significantly decreasing in all Arab countries with the lowest coefficients for Libya, Algeria, and Kuwait and the highest ones for Mauritania, Iraq, and Egypt.

Table 1: Trend of fertility rate in Arab countries

Country R-squared Intercept Fertility Rate

Algeria 0.905 8.565 (48.926)

-0.124 (-22.625)

Bahrain 0.972 7.269 (92.159)

-0.108 (-43.619)

Egypt 0.930 6.789 (75.246)

-0.076 (-26.803)

Iraq 0.869 7.467 (78.218)

-0.057 (-18.949)

Jordan 0.946 8.793 (79.169)

-0.107 (-30.622)

Kuwait 0.884 7.684 (40.945)

-0.119 (-20.265)

Lebanon 0.982 5.722 (115.125)

-0.085 (-54.245)

Libya 0.893 8.900 (45.821)

-0.129 (-21.247)

Mauritania 0.962 7.186 (187.074)

-0045 (-37.090)

Morocco 0.959 7.534 (77.885)

-0.107 (-35.331)

Oman 0.713 9.004 (29.708)

-0.110 (-11.589)

Qatar 0.974 7.701 (98.067)

-0.111 (-45.138)

Saudi

Arabia 0.896 8.414 (55.838)

-0.102 (-21.536)

Sudan 0.883 7.412 (93.777)

-0.049 (-20.176)

Syria 0.941 8.528 (73.162)

-0.107 (-29.266)

Tunisia 0.939 7.422 (57.933)

-0.117 (-29.041)

UAE 0.979 7.545 (104.154)

-0.113 (-49.895)

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Palestine 0.965 10.065 (51.798)

-0.113 (-25.363)

Yemen 0.508 8.984 (32.526)

-0.065 (-7.474)

2. Mortality rates in Arab countries

With regard to the trend of mortality per 1000 infants in Arab economies, they all have negative significant trends. Highest values of the coefficients of the trends are for Mauritania, Iraq and Yemen, meaning that these countries have lower decreasing rates than the remaining countries (Table 2).

Table 2: Trend of mortality of infants (per 1000 infants) in Arab countries

Country R-squared Intercept

Mortality per 1000 live births Algeria 0.892 156.607

(35.138)

-2.947 (-21.089)

Bahrain 0.737 80.824 (18.035)

-1.727 (-12.290)

Egypt 0.953 190.941 (54.834)

-3.630 (-33.253)

Iraq 0.842 98.893 (33.518)

-1.569 (-16.963)

Jordan 0.872 81.351 (34.089)

-1.432 (-19.143)

Kuwait 0.801 68.964 (22.563)

-1.413 (-14.748)

Lebanon 0.991 57.864 (141.467)

-0.989 (-77.093)

Libya 0.867 122.037 (29.698)

-2.417 (-18.762)

Mauritania 0.915 121.577 (80.737)

-1.139 (-24.126)

Morocco 0.983 142.001 (102.911)

-2.385 (-55.139)

Oman 0.844 170.209 (23.056)

-3.665 (-16.430)

Qatar 0.897 48.631 (30.584)

-0.886 (-19.568)

Saudi

Arabia 0.866 108.484 (23.876)

-2.041 (-16.259)

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Sudan 0.980 107.269 (171.325)

-1.021 (-52.038)

Syria 0.889 94.044 (33.954)

-1.807 (-20.814)

Tunisia 0.893 141.904 (32.039)

-2.794 (-20.681)

UAE 0.755 91.572 (18.326)

-2.021 (-12.900)

Palestine 0.875 79.883 (27.419)

-1.274 (-16.319)

Yemen 0.913 247.359 (38.741)

-4.432 (-22.977)

3. Demographic Dividend

Table 3 shows the coefficients of each of the variables from the model resulted from the robust standard error regression process. The log initial working age ratio and the growth rate of working age ratio coefficients are to be used in the estimation of the demographic dividend.

Table 3: Coefficients obtained from the robust standard error regression analysis for Arab countries

Country Intercept Log GDP per capita

Log initial working age

ratio

Growth rate of working

age ratio

Algeria 1.894 -0.813 1.169 0.231

Bahrain -141.601 -21.141 124.564 -0.969

Egypt 23.491 0.424 -12.774 1.049

Iraq -2.250 8.091 -10.932 -3.905

Jordan 36.030 -12.205 4.406 -3.498

Kuwait -134.186 -17.204 112.448 1.841

Lebanon 134.287 4.306 -84.373 7.852

Mauritania -256.248 -4.886 156.992 -3.716

Morocco -56.241 -7.437 46.523 -0.314

Oman -29.080 -13.211 47.900 -2.826

Qatar -72.814 -5.422 51.162 1.495

Saudi Arabia -153.787 -24.695 144.530 -3.847

Sudan -160.092 5.540 86.003 -4.606

Syria -15.450 -1.189 12.909 -1.468

Tunisia -24.238 -4.103 21.718 2.389

United Arab Emirates -32.815 20.413 -32.779 0.373

Palestine 307.447 40.298 -255.883 5.261

Yemen 401.306 21.248 -275.244 7.927

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The resulted demographic dividends are summarized in table 4. The selected basis year to compute the demographic dividend is the year 1960, and results are

summarized to show the values of each 5 years. A negative value of the demographic dividend is interpreted such as there is no increment in the income per capita that is caused or attributed to the change of the working age population. But a positive value indicates the opposite.

Findings divide Arab countries into two main categories that illustrate economies that still have the demographic dividend and countries that don’t. For Algeria, Egypt, Iraq, Jordan, Lebanon, Sudan, United Arab Emirates, Palestine, and Yemen, results

indicate that the windows of opportunities that is caused by the population change no longer exist, as the latest years indicate a negative energy. But for Bahrain, Kuwait, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Syria, and Tunisia, the

demographic dividend started in the years, 1975, 1978, 2005, 1980, 2008, 1960, 1986, 2011, and 1969, respectively. For countries that are still experiencing the

demographic dividend, there are countries that have increasing trends of its

corresponding values while others face the opposite. This gives incentives about the countries that will either have longer periods to benefit from the demographic change or not.

Findings indicate that all these economies have increasing trends except for Qatar, and Tunisia.

Table 4: The demographic dividend in Arab countries

Country 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Algeria -0.17 0.05 0.02 0.06 0.13 0.24 0.39 0.46 0.38 0.12 -0.06 Bahrain -3.10 -3.34 0.70 5.67 7.35 7.86 9.49 10.56 12.94 17.00 17.65 Egypt 0.23 0.25 0.32 0.10 -0.11 0.08 0.57 0.77 0.26 -0.85 -1.08 Iraq 3.12 1.92 2.79 -0.44 -1.31 -0.76 -2.17 -1.97 -1.22 -0.79 -1.49 Jordan 1.88 -0.95 -0.16 0.29 -2.83 -2.06 -6.43 -1.43 -0.97 -0.11 -1.35 Kuwait -2.19 -7.59 -5.33 1.79 2.96 5.83 8.54 8.89 9.76 12.42 13.47 Lebanon 2.13 6.81 5.01 -1.10 2.45 -0.75 -0.24 -2.29 -3.00 -4.04 -8.84 Mauritania -0.62 -2.55 -3.28 -3.03 -2.58 -2.29 -2.09 -1.56 0.06 1.51 2.59 Morocco -1.10 -1.46 -0.93 0.12 0.84 1.35 1.89 2.87 3.71 4.37 4.67 Oman -0.19 -1.14 -1.04 -0.62 -2.00 -4.55 0.46 -0.42 2.92 5.80 Qatar 2.82 3.54 4.68 3.05 6.54 5.81 5.87 6.18 8.32 10.80 9.09 Saudi Arabia -0.13 -0.46 -1.29 -0.45 -0.47 2.20 0.05 3.40 5.72 10.63 15.31 Sudan -0.06 -0.27 -0.74 -1.90 -2.84 -2.39 -1.73 0.31 0.10 0.36 -0.34 Syria 0.00 -0.74 -0.13 0.00 -0.66 -1.22 -1.36 -0.88 0.08 -0.46 1.90

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Tunisia -3.52 0.92 1.56 1.80 2.09 2.47 3.71 4.38 4.46 3.32 1.70 United Arab Emirates -0.99 -2.65 -4.29 -4.62 -4.02 -3.57 -4.22 -4.78 -5.85 -6.95 -7.12

Palestine -0.31 0.23 1.53 -0.54 -6.61 -13.01

Yemen 0.28 -1.74 1.99 11.43 14.62 18.49 23.43 21.08 16.42 6.47 -0.80

II. Causalities of the dependency ratio and economic, educational, and social variables

1. Causality tests of the dependency ratio and unemployment variables in Arab:

Tables 5, 6, 7, 8, and 9 summarize the results of the Granger causality test of the dependency ratio and employment variables in Arab countries. Under a level of significance of 5%, Algeria indicates that the dependency ratio causes the females labor force, causes the total unemployment, and causes the participation of youth in the labor force. This latter variable also causes the dependency ratio. But for Bahrain, the total labor force, the female labor force, and the participation of youth in the total labor force cause the dependency ratio. Egypt does not show any causalities under a 5% significance level. But for Iraq, the dependency ratio causes the female labor force (Table 5).

Table 5: Granger causality of the dependency ratio and employment variables in Arab countries (set1):

Country

Algeria Bahrain Egypt Iraq

F-

statistic Prob. F-

statistic Prob.

LABORFORCETOTAL does not Granger

Cause DR 2.379 0.118 6.993 0.005 0.769 0.476 1.253 0.306

DR does not Granger Cause

LABORFORCETOTAL 2.918 0.077 2.768 0.086 2.477 0.109 1.627 0.221 LABORFORCEFEMALE does not Granger

Cause DR 3.423 0.052 3.591 0.046 0.270 0.765 3.139 0.065

LABORFORCEFEMALE 7.170 0.004 0.002 0.997 1.826 0.186 3.671 0.043 UNEMPLOYMENTTOTAL does not

Granger Cause DR 3.509 0.050 1.271 0.303 0.808 0.460 1.253 0.308 DR does not Granger Cause

UNEMPLOYMENTTOTAL 3.846 0.039 3.518 0.050 3.266 0.060 0.270 0.765 UNEMPLOYMENTYOUNGFEMALE does

not Granger Cause DR 2.813 0.085 0.754 0.483 0.214 0.808 1.284 0.299 DR does not Granger Cause

UNEMPLOYMENTYOUNGFEMALE 3.267 0.060 0.633 0.541 0.360 0.702 0.066 0.936 UNEMPLOYMENTYOUNGMALE does not

UNEMPLOYMENTYOUNGMALE 3.242 0.061 1.936 0.171 3.413 0.054 0.037 0.962 YOUTHLABORFORCEPARTICIPA does

not Granger Cause DR 4.095 0.032 6.759 0.005 0.210 0.812 0.491 0.618

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YOUTHLABORFORCEPARTICIPA 4.403 0.026 2.154 0.142 1.652 0.216 7.631 0.003

For Jordan, there is a double causality between the total unemployment and the dependency ratio while also the unemployment of young males causes the

dependency ratio. For Kuwait, the total labor force and the female labor force cause the dependency ratio. In the case of Lebanon, there is a double causality between the total labor force and the dependency ratio besides this latter variable that causes the young female unemployment and the participation of youth in the labor force. In Libya, the dependency ratio causes the total labor force, the female labor force, the young female unemployment, the young male unemployment, and has a double causality with the youth participation in the labor force (Table 6).

Country

Jordan Kuwait Lebanon Libya

F-

statistic Prob. F-

statistic Prob.

Cause DR 2.96400 0.0746 6.25119 0.0106 7.13377 0.0046 1.61930 0.2229 DR does not Granger Cause

LABORFORCETOTAL 0.49310 0.6180 2.40592 0.1241 18.4160 3.E-05 4.78290 0.0201 LABORFORCEFEMALE does not Granger

LABORFORCEFEMALE 0.47370 0.6295 0.46131 0.6391 2.66138 0.0944 8.97422 0.0017 UNEMPLOYMENTTOTAL does not Granger

UNEMPLOYMENTYOUNGMALE 1.49753 0.2489 0.64545 0.5384 0.97478 0.3954 4.35622 0.0277 YOUTHLABORFORCEPARTICIPA does not

For Mauritania, no causalities are found, but for Morocco, the dependency ratio causes the total unemployment, young females unemployment, young male

unemployment, and youth participation in the labor force. In Oman, the dependency

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ratio has a double causality with the total labor force, is caused by both the female labor force and the total unemployment, and causes the participation of youth in the labor force. For Qatar, the female labor force and the young males unemployment cause the dependency ratio, which causes the participation of youth in the labor force (Table 7).

Country Mauritania Morocco Oman Qatar

F-statistic Prob. F-statistic Prob. F-statistic Prob. F-statistic Prob.

LABORFORCETOTAL 0.10597 0.9000 1.44192 0.2600 17.7143 4.E-05 1.42331 0.2643 LABORFORCEFEMALE does not Granger

In Saudi Arabia, Sudan, and Syria, the dependency ratio causes the total labor force and the female labor force. In addition to that, the dependency ratio also causes the participation of youth in the labor force in Sudan and Syria. The dependency ratio causes the total labor force and the participation of youth in the labor force in Tunisia (Table 8).

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Country

Saudi Arabia Sudan Syria Tunisia

F-

statistic Prob. F-

statistic Prob.

LABORFORCETOTAL 4.16537 0.0307 6.37552 0.0072 7.10353 0.0047 4.24435 0.0291 LABORFORCEFEMALE does not Granger

In the United Arab Emirates, the dependency ratio causes all unemployment and labor force variables except the female labor force. But in Palestine, the dependency ratio causes total labor force, has a double causality with the female labor force, and is caused by the participation of youth in the labor market. In the case of Yemen, the dependency ratio has a double causality with the total labor force and the young females unemployment and is caused by the female labor force, the total

unemployment, and the participation of youth in the labor market (Table 9).

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Country

United Arab Emirates Palestine Yemen F-

statistic Prob. F-

statistic Prob.

Cause DR 0.60452 0.5560 4.98884 0.0175 3.67455 0.0437

LABORFORCETOTAL 8.13669 0.0026 1.46612 0.2546 6.87337 0.0053 LABORFORCEFEMALE does not Granger

Cause DR 1.97080 0.1655 5.89189 0.0097 3.53901 0.0483

LABORFORCEFEMALE 2.38456 0.1178 4.84021 0.0193 2.12714 0.1454 UNEMPLOYMENTTOTAL does not Granger

Cause DR 1.02806 0.3768 0.43938 0.6508 6.83202 0.0058

UNEMPLOYMENTTOTAL 6.05255 0.0093 0.86914 0.4353 6.14295 0.0088 UNEMPLOYMENTYOUNGFEMALE does

not Granger Cause DR 1.22771 0.3152 1.75505 0.1998 8.01779 0.0030 DR does not Granger Cause

UNEMPLOYMENTYOUNGFEMALE 3.75634 0.0422 2.75257 0.0892 5.18518 0.0160 UNEMPLOYMENTYOUNGMALE does not

Granger Cause DR 1.27242 0.3030 0.82733 0.4524 2.46454 0.1118 DR does not Granger Cause

UNEMPLOYMENTYOUNGMALE 6.00354 0.0095 1.40564 0.2696 0.29724 0.7463 YOUTHLABORFORCEPARTICIPA does not

YOUTHLABORFORCEPARTICIPA 7.91117 0.0029 6.77374 0.0057 1.30675 0.2928

2. Granger causality between the dependency ratio and economic development variables in Arab countries:

Table 10, 11, 12, 13, and 14 summarizes the causal links between the dependency ratio and economic development variables in Arab economies.

The dependency ratio causes the GDP per capita growth and is caused by gross savings and agriculture value added. In Bahrain, the dependency ratio causes the agriculture value added, and is caused by the gross savings and the industry value added. In Egypt the dependency ratio only cause the industry value added. In the case of Jordan, the dependency ratio is caused by both the GDP per capita growth and the agriculture value added (Table 10).

Table 10: Granger causality of the dependency ratio and economic development variables in Arab countries (set1):

Country Algeria Bahrain Egypt Jordan

F-statistic Prob. F-statistic Prob. F-statistic Prob. F-statistic Prob.

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GDPPERCAPITA does not Granger Cause

DR 0.55054 0.5801 1.05173 0.3623 1.46725 0.2403 0.27127 0.764

GDPPERCAPITA 0.92571 0.4029 0.42452 0.6581 1.0503 0.3574 0.45856 0.6359 GDPPERCAPITAGROWTH does not

GDPPERCAPITAGROWTH 4.11829 0.0222 0.65934 0.525 0.18199 0.8342 0.04271 0.9582 GROSSSAVINGS does not Granger Cause

DR 5.85945 0.0116 5.96396 0.007 0.48339 0.6211 0.0652 0.937

GROSSSAVINGS 0.98483 0.3938 0.91666 0.4115 1.7339 0.1928 1.03705 0.3658 AGRICULTUREVALUEADDED does not

AGRICULTUREVALUEADDED 0.2134 0.8086 5.37314 0.0199 0.55086 0.5803 1.78469 0.1795 INDUSTRYVALUEADDED does not

INDUSTRYVALUEADDED 2.00606 0.1464 0.03836 0.9625 3.7075 0.0323 0.49738 0.6114

In Kuwait, no causal links are found, Mauritania, the dependency ratio is caused by the GDP per capita growth, and in Morocco, the dependency ratio is caused by the industry value added. For Lebanon, the dependency ratio causes the GDP per capita, the GDP per capita growth, and has a double causality with the gross savings (Table 11).

Country

Kuwait Lebanon Mauritania Morocco

F-

statistic Prob. F-

statistic Prob.

GDPPERCAPITA does not Granger

GDPPERCAPITA 0.84452 0.4379 12.3885 0.0002 1.27529 0.2883 0.64548 0.5293 GDPPERCAPITAGROWTH does not

GDPPERCAPITAGROWTH 0.92071 0.4077 6.50199 0.0063 2.0534 0.1392 0.03644 0.9642 GROSSSAVINGS does not Granger

GROSSSAVINGS 0.67911 0.5149 22.0782 0.0009 1.31684 0.2914 1.15294 0.3274 AGRICULTUREVALUEADDED does

not Granger Cause DR NA NA 1.06613 0.3676 2.43247 0.0981 3.20328 0.0548 DR does not Granger Cause

AGRICULTUREVALUEADDED NA NA 0.74223 0.4917 1.06229 0.3533 1.55592 0.2275 INDUSTRYVALUEADDED does not

Granger Cause DR NA NA 1.31498 0.296 1.31063 0.2788 4.34856 0.022 DR does not Granger Cause

INDUSTRYVALUEADDED NA NA 0.3358 0.7197 0.61426 0.5451 1.11104 0.3424

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Oman and Saudi Arabia do not show significant causal relationships under a level of significance of 5%, and in Qatar only the industry value added causes the dependency ratio (Table 12).

Country

Oman Qatar Saudi Arabia

F-

statistic Prob. F-

statistic Prob. F-statistic Prob.

Cause DR 1.88423 0.164 2.3019 0.1505 0.02259 0.9777

GDPPERCAPITA 0.49156 0.615 1.90869 0.1986 2.94071 0.0638 GDPPERCAPITAGROWTH does not

GDPPERCAPITAGROWTH 0.98988 0.3799 0.93368 0.4281 0.43353 0.6512 GROSSSAVINGS does not Granger

Cause DR 0.68712 0.5103 NA NA 1.78787 0.1808

GROSSSAVINGS 0.09448 0.9101 NA NA 0.02138 0.9789

AGRICULTUREVALUEADDED does

not Granger Cause DR 0.06478 0.9375 3.84843 0.0576 0.90099 0.4139 DR does not Granger Cause

AGRICULTUREVALUEADDED 0.05106 0.9504 0.31167 0.7391 1.15765 0.324 INDUSTRYVALUEADDED does not

INDUSTRYVALUEADDED 0.67732 0.5205 0.10423 0.902 0.68406 0.5101

In Syria, the dependency ratio causes both the agriculture value added and the industry value added, and is caused by both the GDP per capita growth and the gross savings. In Tunisia, the dependency ratio has a double causality with the GDP per capita, and causes the gross savings besides the agriculture value added. No significant causal relationship is found for Sudan (Table 13).

Country

Sudan Syria Tunisia

F-

statistic Prob. F-

statistic Prob.

Cause DR 1.1331 0.3302 0.90029 0.4143 23.0598 0.0000001